Overview

Dataset statistics

Number of variables14
Number of observations66
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory116.0 B

Variable types

Numeric13
Categorical1

Alerts

kmeans_labels has constant value ""Constant
Flavanoids is highly overall correlated with Proanthocyanins and 2 other fieldsHigh correlation
Proanthocyanins is highly overall correlated with Flavanoids and 1 other fieldsHigh correlation
Proline is highly overall correlated with FlavanoidsHigh correlation
Total_Phenols is highly overall correlated with Flavanoids and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-11-28 13:21:47.578427
Analysis finished2023-11-28 13:22:19.017286
Duration31.44 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Alcohol
Real number (ℝ)

Distinct55
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.705
Minimum12.99
Maximum14.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:19.155128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.99
5-th percentile13.05
Q113.345
median13.725
Q314.05
95-th percentile14.3775
Maximum14.83
Range1.84
Interquartile range (IQR)0.705

Descriptive statistics

Standard deviation0.44430845
Coefficient of variation (CV)0.032419442
Kurtosis-0.46732662
Mean13.705
Median Absolute Deviation (MAD)0.35
Skewness0.27341315
Sum904.53
Variance0.19741
MonotonicityNot monotonic
2023-11-28T13:22:19.356170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.05 4
 
6.1%
13.58 2
 
3.0%
13.86 2
 
3.0%
13.56 2
 
3.0%
13.83 2
 
3.0%
13.11 2
 
3.0%
14.1 2
 
3.0%
13.48 2
 
3.0%
14.06 2
 
3.0%
12.99 1
 
1.5%
Other values (45) 45
68.2%
ValueCountFrequency (%)
12.99 1
 
1.5%
13.03 1
 
1.5%
13.05 4
6.1%
13.07 1
 
1.5%
13.11 2
3.0%
13.16 1
 
1.5%
13.17 1
 
1.5%
13.2 1
 
1.5%
13.24 1
 
1.5%
13.28 1
 
1.5%
ValueCountFrequency (%)
14.83 1
1.5%
14.75 1
1.5%
14.39 1
1.5%
14.38 1
1.5%
14.37 1
1.5%
14.34 1
1.5%
14.3 1
1.5%
14.23 1
1.5%
14.22 1
1.5%
14.2 1
1.5%

Malic_Acid
Real number (ℝ)

Distinct46
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7860606
Minimum0.9
Maximum2.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:19.578863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.275
Q11.65
median1.73
Q31.8975
95-th percentile2.575
Maximum2.76
Range1.86
Interquartile range (IQR)0.2475

Descriptive statistics

Standard deviation0.35768004
Coefficient of variation (CV)0.20026198
Kurtosis1.5865193
Mean1.7860606
Median Absolute Deviation (MAD)0.135
Skewness0.49777494
Sum117.88
Variance0.12793501
MonotonicityNot monotonic
2023-11-28T13:22:19.778739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1.73 5
 
7.6%
1.68 3
 
4.5%
1.67 3
 
4.5%
1.9 3
 
4.5%
1.81 3
 
4.5%
1.65 2
 
3.0%
1.66 2
 
3.0%
1.5 2
 
3.0%
1.77 2
 
3.0%
1.71 2
 
3.0%
Other values (36) 39
59.1%
ValueCountFrequency (%)
0.9 1
1.5%
0.94 1
1.5%
1.01 1
1.5%
1.25 1
1.5%
1.35 1
1.5%
1.43 1
1.5%
1.48 1
1.5%
1.5 2
3.0%
1.51 1
1.5%
1.53 1
1.5%
ValueCountFrequency (%)
2.76 1
1.5%
2.59 2
3.0%
2.58 1
1.5%
2.56 1
1.5%
2.51 1
1.5%
2.36 1
1.5%
2.16 1
1.5%
2.15 1
1.5%
2.05 1
1.5%
2.02 1
1.5%

Ash
Real number (ℝ)

Distinct46
Distinct (%)69.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4392424
Minimum1.7
Maximum3.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:19.991930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile2.055
Q12.3
median2.425
Q32.635
95-th percentile2.7875
Maximum3.22
Range1.52
Interquartile range (IQR)0.335

Descriptive statistics

Standard deviation0.26496044
Coefficient of variation (CV)0.10862407
Kurtosis1.1565215
Mean2.4392424
Median Absolute Deviation (MAD)0.18
Skewness-0.27604017
Sum160.99
Variance0.070204033
MonotonicityNot monotonic
2023-11-28T13:22:20.183290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2.7 4
 
6.1%
2.36 4
 
6.1%
2.3 3
 
4.5%
2.5 2
 
3.0%
2.6 2
 
3.0%
2.45 2
 
3.0%
2.61 2
 
3.0%
2.1 2
 
3.0%
2.27 2
 
3.0%
2.67 2
 
3.0%
Other values (36) 41
62.1%
ValueCountFrequency (%)
1.7 1
1.5%
1.71 1
1.5%
1.92 1
1.5%
2.04 1
1.5%
2.1 2
3.0%
2.12 1
1.5%
2.14 2
3.0%
2.17 1
1.5%
2.21 1
1.5%
2.24 1
1.5%
ValueCountFrequency (%)
3.22 1
 
1.5%
2.87 1
 
1.5%
2.84 1
 
1.5%
2.8 1
 
1.5%
2.75 1
 
1.5%
2.72 1
 
1.5%
2.7 4
6.1%
2.69 1
 
1.5%
2.68 2
3.0%
2.67 2
3.0%

Ash_Alcanity
Real number (ℝ)

Distinct41
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.086364
Minimum11.2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:20.383371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.2
5-th percentile12.8
Q116
median17.15
Q320
95-th percentile25
Maximum30
Range18.8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.5777469
Coefficient of variation (CV)0.1978146
Kurtosis1.1916217
Mean18.086364
Median Absolute Deviation (MAD)1.75
Skewness0.84701567
Sum1193.7
Variance12.800273
MonotonicityNot monotonic
2023-11-28T13:22:20.577482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
16 6
 
9.1%
20 6
 
9.1%
16.8 3
 
4.5%
17 3
 
4.5%
18 3
 
4.5%
17.2 3
 
4.5%
25 3
 
4.5%
15.5 2
 
3.0%
15 2
 
3.0%
22.5 2
 
3.0%
Other values (31) 33
50.0%
ValueCountFrequency (%)
11.2 1
1.5%
11.4 1
1.5%
12 1
1.5%
12.4 1
1.5%
14 2
3.0%
14.6 1
1.5%
15 2
3.0%
15.2 1
1.5%
15.5 2
3.0%
15.6 1
1.5%
ValueCountFrequency (%)
30 1
 
1.5%
25.5 1
 
1.5%
25 3
4.5%
24.5 1
 
1.5%
24 1
 
1.5%
22.5 2
 
3.0%
22 1
 
1.5%
21 1
 
1.5%
20.5 2
 
3.0%
20 6
9.1%

Magnesium
Real number (ℝ)

Distinct37
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.62121
Minimum78
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:25.352639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile87.5
Q196
median102.5
Q3112.75
95-th percentile125.5
Maximum139
Range61
Interquartile range (IQR)16.75

Descriptive statistics

Standard deviation12.317299
Coefficient of variation (CV)0.11773233
Kurtosis-0.074133116
Mean104.62121
Median Absolute Deviation (MAD)8.5
Skewness0.41322933
Sum6905
Variance151.71585
MonotonicityNot monotonic
2023-11-28T13:22:25.537282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
101 5
 
7.6%
98 4
 
6.1%
110 3
 
4.5%
118 3
 
4.5%
108 3
 
4.5%
96 3
 
4.5%
94 3
 
4.5%
89 3
 
4.5%
91 2
 
3.0%
100 2
 
3.0%
Other values (27) 35
53.0%
ValueCountFrequency (%)
78 1
 
1.5%
86 2
3.0%
87 1
 
1.5%
89 3
4.5%
90 1
 
1.5%
91 2
3.0%
92 1
 
1.5%
93 1
 
1.5%
94 3
4.5%
95 1
 
1.5%
ValueCountFrequency (%)
139 1
 
1.5%
132 1
 
1.5%
127 1
 
1.5%
126 1
 
1.5%
124 1
 
1.5%
121 1
 
1.5%
120 2
3.0%
118 3
4.5%
117 1
 
1.5%
116 2
3.0%

Total_Phenols
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7077273
Minimum1.35
Maximum3.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:25.756591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.35
5-th percentile1.6575
Q12.5075
median2.8
Q32.995
95-th percentile3.3
Maximum3.88
Range2.53
Interquartile range (IQR)0.4875

Descriptive statistics

Standard deviation0.50502337
Coefficient of variation (CV)0.1865119
Kurtosis1.0588011
Mean2.7077273
Median Absolute Deviation (MAD)0.2
Skewness-0.64627461
Sum178.71
Variance0.2550486
MonotonicityNot monotonic
2023-11-28T13:22:25.987805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2.8 6
 
9.1%
2.6 5
 
7.6%
2.95 5
 
7.6%
3 5
 
7.6%
3.3 3
 
4.5%
2.53 2
 
3.0%
2.98 2
 
3.0%
2.2 2
 
3.0%
3.1 2
 
3.0%
2.85 2
 
3.0%
Other values (29) 32
48.5%
ValueCountFrequency (%)
1.35 1
1.5%
1.4 1
1.5%
1.55 1
1.5%
1.65 1
1.5%
1.68 1
1.5%
1.88 1
1.5%
1.95 1
1.5%
2.1 1
1.5%
2.2 2
3.0%
2.35 1
1.5%
ValueCountFrequency (%)
3.88 1
 
1.5%
3.85 1
 
1.5%
3.4 1
 
1.5%
3.3 3
4.5%
3.27 1
 
1.5%
3.25 1
 
1.5%
3.2 1
 
1.5%
3.15 1
 
1.5%
3.1 2
 
3.0%
3 5
7.6%

Flavanoids
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6812121
Minimum0.5
Maximum3.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:26.187023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.735
Q12.5125
median2.895
Q33.22
95-th percentile3.6625
Maximum3.93
Range3.43
Interquartile range (IQR)0.7075

Descriptive statistics

Standard deviation0.82682435
Coefficient of variation (CV)0.30837708
Kurtosis0.83629469
Mean2.6812121
Median Absolute Deviation (MAD)0.36
Skewness-1.2098184
Sum176.96
Variance0.68363851
MonotonicityNot monotonic
2023-11-28T13:22:26.391852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 2
 
3.0%
2.68 2
 
3.0%
0.68 2
 
3.0%
2.69 2
 
3.0%
3.39 2
 
3.0%
2.76 2
 
3.0%
2.98 2
 
3.0%
2.43 2
 
3.0%
3.74 1
 
1.5%
2.78 1
 
1.5%
Other values (48) 48
72.7%
ValueCountFrequency (%)
0.5 1
1.5%
0.68 2
3.0%
0.7 1
1.5%
0.84 1
1.5%
1.1 1
1.5%
1.28 1
1.5%
1.3 1
1.5%
1.31 1
1.5%
1.79 1
1.5%
1.84 1
1.5%
ValueCountFrequency (%)
3.93 1
1.5%
3.74 1
1.5%
3.69 1
1.5%
3.67 1
1.5%
3.64 1
1.5%
3.56 1
1.5%
3.54 1
1.5%
3.49 1
1.5%
3.4 1
1.5%
3.39 2
3.0%

Nonflavanoid_Phenols
Real number (ℝ)

Distinct27
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31181818
Minimum0.17
Maximum0.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:26.584248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile0.2025
Q10.26
median0.29
Q30.34
95-th percentile0.515
Maximum0.55
Range0.38
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.09062024
Coefficient of variation (CV)0.29061885
Kurtosis0.47094364
Mean0.31181818
Median Absolute Deviation (MAD)0.05
Skewness0.93336473
Sum20.58
Variance0.008212028
MonotonicityNot monotonic
2023-11-28T13:22:26.761409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.26 7
 
10.6%
0.29 6
 
9.1%
0.32 5
 
7.6%
0.28 4
 
6.1%
0.21 4
 
6.1%
0.34 4
 
6.1%
0.22 4
 
6.1%
0.27 3
 
4.5%
0.39 3
 
4.5%
0.24 3
 
4.5%
Other values (17) 23
34.8%
ValueCountFrequency (%)
0.17 2
 
3.0%
0.19 1
 
1.5%
0.2 1
 
1.5%
0.21 4
6.1%
0.22 4
6.1%
0.24 3
4.5%
0.26 7
10.6%
0.27 3
4.5%
0.28 4
6.1%
0.29 6
9.1%
ValueCountFrequency (%)
0.55 1
1.5%
0.53 2
3.0%
0.52 1
1.5%
0.5 1
1.5%
0.47 1
1.5%
0.44 1
1.5%
0.43 1
1.5%
0.42 1
1.5%
0.41 1
1.5%
0.4 1
1.5%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8259091
Minimum0.42
Maximum2.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:27.087188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile1.03
Q11.48
median1.86
Q32.095
95-th percentile2.7825
Maximum2.96
Range2.54
Interquartile range (IQR)0.615

Descriptive statistics

Standard deviation0.51331795
Coefficient of variation (CV)0.28113007
Kurtosis0.49872208
Mean1.8259091
Median Absolute Deviation (MAD)0.32
Skewness-0.15841014
Sum120.51
Variance0.26349531
MonotonicityNot monotonic
2023-11-28T13:22:27.463986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2.29 3
 
4.5%
1.46 3
 
4.5%
1.97 3
 
4.5%
1.98 3
 
4.5%
2.38 3
 
4.5%
1.03 2
 
3.0%
1.87 2
 
3.0%
2.03 2
 
3.0%
1.35 2
 
3.0%
1.66 2
 
3.0%
Other values (38) 41
62.1%
ValueCountFrequency (%)
0.42 1
1.5%
0.64 1
1.5%
0.73 1
1.5%
1.03 2
3.0%
1.24 1
1.5%
1.25 1
1.5%
1.28 1
1.5%
1.35 2
3.0%
1.36 1
1.5%
1.37 1
1.5%
ValueCountFrequency (%)
2.96 1
 
1.5%
2.91 1
 
1.5%
2.81 2
3.0%
2.7 1
 
1.5%
2.45 1
 
1.5%
2.38 3
4.5%
2.34 1
 
1.5%
2.29 3
4.5%
2.28 1
 
1.5%
2.18 1
 
1.5%

Color_Intensity
Real number (ℝ)

Distinct60
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9210606
Minimum3.17
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:27.868448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.17
5-th percentile3.535
Q14.6
median5.62
Q36.7875
95-th percentile9.51
Maximum13
Range9.83
Interquartile range (IQR)2.1875

Descriptive statistics

Standard deviation1.9544782
Coefficient of variation (CV)0.33008921
Kurtosis2.3841173
Mean5.9210606
Median Absolute Deviation (MAD)1.125
Skewness1.3082737
Sum390.79
Variance3.819985
MonotonicityNot monotonic
2023-11-28T13:22:28.268813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.6 3
 
4.5%
6.2 2
 
3.0%
3.8 2
 
3.0%
5.4 2
 
3.0%
4.6 2
 
3.0%
5.04 1
 
1.5%
6.1 1
 
1.5%
8.9 1
 
1.5%
6.38 1
 
1.5%
7.05 1
 
1.5%
Other values (50) 50
75.8%
ValueCountFrequency (%)
3.17 1
1.5%
3.35 1
1.5%
3.38 1
1.5%
3.52 1
1.5%
3.58 1
1.5%
3.7 1
1.5%
3.74 1
1.5%
3.8 2
3.0%
3.84 1
1.5%
3.95 1
1.5%
ValueCountFrequency (%)
13 1
1.5%
11.75 1
1.5%
9.7 1
1.5%
9.58 1
1.5%
9.3 1
1.5%
8.9 1
1.5%
8.7 1
1.5%
8.66 1
1.5%
7.8 1
1.5%
7.5 1
1.5%

Hue
Real number (ℝ)

Distinct38
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0359091
Minimum0.57
Maximum1.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:28.635757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.57
5-th percentile0.6125
Q10.9575
median1.07
Q31.145
95-th percentile1.25
Maximum1.36
Range0.79
Interquartile range (IQR)0.1875

Descriptive statistics

Standard deviation0.18609325
Coefficient of variation (CV)0.17964246
Kurtosis0.62172238
Mean1.0359091
Median Absolute Deviation (MAD)0.095
Skewness-0.97204181
Sum68.37
Variance0.034630699
MonotonicityNot monotonic
2023-11-28T13:22:28.865029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1.12 5
 
7.6%
1.04 4
 
6.1%
1.25 4
 
6.1%
1.13 3
 
4.5%
1.02 3
 
4.5%
1.09 3
 
4.5%
1.23 3
 
4.5%
1.07 3
 
4.5%
0.57 2
 
3.0%
0.7 2
 
3.0%
Other values (28) 34
51.5%
ValueCountFrequency (%)
0.57 2
3.0%
0.6 1
1.5%
0.61 1
1.5%
0.62 1
1.5%
0.7 2
3.0%
0.74 1
1.5%
0.86 1
1.5%
0.88 2
3.0%
0.89 1
1.5%
0.91 1
1.5%
ValueCountFrequency (%)
1.36 1
 
1.5%
1.31 1
 
1.5%
1.28 1
 
1.5%
1.25 4
6.1%
1.24 1
 
1.5%
1.23 3
4.5%
1.2 1
 
1.5%
1.19 2
3.0%
1.18 1
 
1.5%
1.17 1
 
1.5%

OD280
Real number (ℝ)

Distinct54
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9293939
Minimum1.33
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:29.230403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.33
5-th percentile1.7275
Q12.735
median2.92
Q33.345
95-th percentile3.68
Maximum4
Range2.67
Interquartile range (IQR)0.61

Descriptive statistics

Standard deviation0.57658111
Coefficient of variation (CV)0.19682608
Kurtosis0.56288605
Mean2.9293939
Median Absolute Deviation (MAD)0.32
Skewness-0.78778204
Sum193.34
Variance0.33244578
MonotonicityNot monotonic
2023-11-28T13:22:29.566549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87 3
 
4.5%
3.4 2
 
3.0%
2.78 2
 
3.0%
3.17 2
 
3.0%
3.2 2
 
3.0%
2.93 2
 
3.0%
2.85 2
 
3.0%
3.58 2
 
3.0%
2.82 2
 
3.0%
3 2
 
3.0%
Other values (44) 45
68.2%
ValueCountFrequency (%)
1.33 1
1.5%
1.62 1
1.5%
1.68 1
1.5%
1.71 1
1.5%
1.78 1
1.5%
1.8 1
1.5%
1.93 1
1.5%
1.96 1
1.5%
2.46 1
1.5%
2.47 1
1.5%
ValueCountFrequency (%)
4 1
1.5%
3.92 1
1.5%
3.82 1
1.5%
3.71 1
1.5%
3.59 1
1.5%
3.58 2
3.0%
3.56 1
1.5%
3.55 1
1.5%
3.5 1
1.5%
3.47 1
1.5%

Proline
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1031.3333
Minimum392
Maximum1680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-11-28T13:22:29.802641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum392
5-th percentile479.5
Q1832.5
median1055
Q31268.75
95-th percentile1502.5
Maximum1680
Range1288
Interquartile range (IQR)436.25

Descriptive statistics

Standard deviation297.61717
Coefficient of variation (CV)0.28857515
Kurtosis-0.38462492
Mean1031.3333
Median Absolute Deviation (MAD)220
Skewness-0.26505925
Sum68068
Variance88575.979
MonotonicityNot monotonic
2023-11-28T13:22:30.022805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1285 3
 
4.5%
1095 2
 
3.0%
1045 2
 
3.0%
1150 2
 
3.0%
750 2
 
3.0%
1280 2
 
3.0%
1035 2
 
3.0%
780 2
 
3.0%
1060 2
 
3.0%
985 2
 
3.0%
Other values (44) 45
68.2%
ValueCountFrequency (%)
392 1
1.5%
410 1
1.5%
425 1
1.5%
472 1
1.5%
502 1
1.5%
615 1
1.5%
620 1
1.5%
630 1
1.5%
660 2
3.0%
735 1
1.5%
ValueCountFrequency (%)
1680 1
1.5%
1547 1
1.5%
1515 1
1.5%
1510 1
1.5%
1480 1
1.5%
1450 1
1.5%
1375 1
1.5%
1320 1
1.5%
1310 1
1.5%
1295 1
1.5%

kmeans_labels
Categorical

CONSTANT 

Distinct1
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
0
66 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters66
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66
100.0%

Length

2023-11-28T13:22:30.202735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-28T13:22:30.358199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 66
100.0%

Most occurring characters

ValueCountFrequency (%)
0 66
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66
100.0%

Interactions

2023-11-28T13:22:15.998388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:47.869045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:51.136965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:53.273797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:55.366241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:57.464622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:59.548959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:02.023771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:04.839850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:07.013661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:09.073055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:11.038248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:13.150651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:16.294004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:48.038398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:51.299759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:53.425820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:55.533291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:57.627028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:59.716606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:02.318351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:05.003870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:07.170713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:09.234378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:11.212332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:13.317447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:16.606819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:48.205668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:51.464586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:53.597412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:55.707008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:57.789030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:59.875363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:02.619623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:05.170779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:07.348237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:09.382515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:11.388089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:13.476407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:16.832875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:48.443528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:51.640402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:53.762121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:55.862453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:57.946555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:00.037035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:02.905380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:05.348705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:07.496410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:09.538426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:11.549532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:13.624284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:17.009062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:48.762057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:51.817166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:53.929720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:56.057703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:58.125793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:00.215151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:03.146410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:05.514747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:07.661050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:09.704337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:11.713537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:13.779031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:17.175417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:49.022056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:51.999637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:54.097692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:56.214718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:58.287354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:00.365216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:03.419023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:05.680986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:07.840887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:09.853476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:11.861109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:13.984728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:17.317946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:49.320323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:52.156271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:54.255865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:56.377165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:58.432418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:00.499777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:03.694285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:05.861760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:07.987152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:09.991687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:12.011303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:14.212174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:17.476749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:49.587386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:52.319377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:54.408409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:56.536619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:58.592432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:00.654161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:03.916186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:06.041290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:08.144998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:10.150741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:12.187738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:14.472310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:17.644001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:49.882222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:52.488161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:54.564262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:56.709023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:58.775726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:00.811856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:04.081625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:06.215144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:08.322000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:10.314956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:12.362014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:14.762200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:17.798704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:50.158710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:52.634085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:54.717402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:56.858795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:58.921218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:00.958829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:04.239257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:06.380569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:08.461234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:10.476809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:12.516918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:14.988894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:17.950948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:50.417754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:52.800516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:54.870952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:57.008364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:59.075698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:01.200681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:04.371929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:06.525251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:08.595226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:10.606720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:12.676502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:15.263961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:18.127837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:50.689259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:52.973176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:55.045688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:57.168486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:59.232481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:01.441658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:04.541028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:06.701265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:08.765660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:10.759750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:12.832919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:15.511317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:18.303176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:50.973615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:53.119856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:55.201209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:57.311360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:21:59.392267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:01.727494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:04.684345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:06.847035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:08.918908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:10.893416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:12.995087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-28T13:22:15.734791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-28T13:22:30.477036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AlcoholAshAsh_AlcanityColor_IntensityFlavanoidsHueMagnesiumMalic_AcidNonflavanoid_PhenolsOD280ProanthocyaninsProlineTotal_Phenols
Alcohol1.0000.019-0.2360.3680.3390.0340.0660.0390.0460.1650.3410.3790.320
Ash0.0191.0000.4970.090-0.0440.0640.3660.3240.379-0.091-0.0190.1240.103
Ash_Alcanity-0.2360.4971.0000.018-0.450-0.0880.1300.2470.256-0.342-0.168-0.336-0.309
Color_Intensity0.3680.0900.0181.0000.269-0.3990.1440.2230.123-0.2530.3360.2390.254
Flavanoids0.339-0.044-0.4500.2691.0000.2420.236-0.121-0.3220.4210.6170.5480.850
Hue0.0340.064-0.088-0.3990.2421.000-0.016-0.299-0.0750.1210.0730.2950.140
Magnesium0.0660.3660.1300.1440.236-0.0161.0000.1840.1070.2220.0850.2160.368
Malic_Acid0.0390.3240.2470.223-0.121-0.2990.1841.0000.3030.040-0.0310.076-0.141
Nonflavanoid_Phenols0.0460.3790.2560.123-0.322-0.0750.1070.3031.000-0.477-0.242-0.152-0.231
OD2800.165-0.091-0.342-0.2530.4210.1210.2220.040-0.4771.0000.3060.2140.442
Proanthocyanins0.341-0.019-0.1680.3360.6170.0730.085-0.031-0.2420.3061.0000.2460.578
Proline0.3790.124-0.3360.2390.5480.2950.2160.076-0.1520.2140.2461.0000.416
Total_Phenols0.3200.103-0.3090.2540.8500.1400.368-0.141-0.2310.4420.5780.4161.000

Missing values

2023-11-28T13:22:18.543886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-28T13:22:18.882728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_labels
014.231.712.4315.61272.803.060.282.295.641.043.9210650
113.201.782.1411.21002.652.760.261.284.381.053.4010500
213.162.362.6718.61012.803.240.302.815.681.033.1711850
314.371.952.5016.81133.853.490.242.187.800.863.4514800
413.242.592.8721.01182.802.690.391.824.321.042.937350
514.201.762.4515.21123.273.390.341.976.751.052.8514500
614.391.872.4514.6962.502.520.301.985.251.023.5812900
714.062.152.6117.61212.602.510.311.255.051.063.5812950
814.831.642.1714.0972.802.980.291.985.201.082.8510450
913.861.352.2716.0982.983.150.221.857.221.013.5510450
AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_labels
7312.991.672.6030.01393.302.890.211.963.351.313.509850
7613.030.901.7116.0861.952.030.241.464.601.192.483920
14113.362.562.3520.0891.400.500.370.645.600.702.477800
15213.111.902.7525.51162.201.280.261.567.100.611.334250
15814.341.682.7025.0982.801.310.532.7013.000.571.966600
15913.481.672.6422.5892.601.100.522.2911.750.571.786200
16413.782.762.3022.0901.350.680.411.039.580.701.686150
16813.582.582.6924.51051.550.840.391.548.660.741.807500
17214.162.512.4820.0911.680.700.441.249.700.621.716600
17613.172.592.3720.01201.650.680.531.469.300.601.628400